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This Solicitation opportunity from Government of Canada was posted on February 19, 2025. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

Integrated Satellite Tracking of Pollution (ISTOP) Machine Learning (ML) Prototype

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WS4896298130International

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This solicitation, issued by Public Services and Procurement Canada on behalf of Environment and Climate Change Canada, seeks the development of an Integrated Satellite Tracking of Pollution (ISTOP) Machine Learning prototype. The project requires a stand-alone proof-of-concept solution utilizing state-of-the-art machine learning to detect pollution-related anomalies within RCM and Sentinel-1 C-band SAR satellite imagery. Performance of the contract is expected to take place within the National Capital Region. Participation is strictly limited to invited qualified suppliers listed in Bands 1, 2, and 3 of the AI-IA Source List EN578-180001. Interested and eligible parties must submit their responses by the deadline of February 28, 2025, at 2:00 PM. The procurement process is managed under solicitation number WS4896298130.

General Info

Develop an ISTOP machine learning prototype for satellite pollution detection by February 28, 2025.

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Timeline

PhaseClosed
Posted

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Submission Closed

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Organization & Contact Information

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AgencyGovernment of Canada → Department of Public Works and Government Services
Contacts1 person available
OfficeN/A
Office AddressN/A
Contacts
Lessard Peter (SPAC/PSPCPoint of Contact

Full Description

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This solicitation is open to the qualified suppliers in Bands 1, 2 and 3 of AI-IA Source List EN578-180001. Qualified suppliers may not submit a bid in response to this bid solicitation unless they have been invited to do so. Public Services and Procurement Canada (PSPC) established the Government of Canada’s first Artificial Intelligence (AI) Source List (EN578-180001/B) in anticipation of Canada’s need for AI goods, services and solutions. The attached Request for Proposal (RFP), for Environment and Climate Change Canada is for an “Integrated Satellite Tracking of Pollution (ISTOP) Machine Learning (ML) Prototype”.The requirement is to develop and deliver a ‘stand-alone proof-of-concept prototype solution’ (hereafter ‘prototype solution’), based on state-of-the-art machine-learning approaches, to detect pollution-related anomalies in RCM and Sentinel-1 C-band SAR satellite imagery.

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Miguel
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Keith Deutsch
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